Add to ORKGWe consider filters for the detection and extraction of compact sources on a background. We make a one-dimensional treatment (though a generalization to two or more dimensions is possible) assuming that the sources have a Gaussian profile whereas the background is modeled by an homogeneous and isotropic Gaussian random field, characterized by a scale-free power spectrum. Local peak detection is used after filtering. Then, a Bayesian Generalized Neyman-Pearson test is used to define the region of acceptance that includes not only the amplification but also the curvature of the sources and the a priori probability distribution function of the sources. We search for an optimal filter between a family of Matched-type filters (MTF) modifying the...
We derive the expression of an optimum non-Gaussian radar detector from the non-Gaussian spherically...
This letter focuses on the design of selective receivers for homogeneous scenarios where a very smal...
Abstract—This paper introduces a Bayesian framework to detect multiple signals embedded in noisy obs...
This paper considers the problem of compact source detection on a Gaussian background in 1D. Two asp...
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) t...
For several reasons, Bayesian parameter estimation is superior to other methods for extracting featu...
The design approach presented in this paper applies Bayesian inference to the design of finite impul...
The design approach presented in this paper applies Bayesian inference to the design of finite impul...
In this dissertation we explore theoretical and computational methods to investigate Bayesian ideal ...
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed ...
In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clut...
International audienceThis paper introduces a Bayesian framework to detect multiple signals embedded...
International audienceWe consider the adaptive detection of a signal of interest embedded in colored...
International audienceWe derive the expression of an optimum non-Gaussian radar detector from the no...
In this paper, a theoretical expression of the optimum non-Gaussian radar detector is derived from t...
We derive the expression of an optimum non-Gaussian radar detector from the non-Gaussian spherically...
This letter focuses on the design of selective receivers for homogeneous scenarios where a very smal...
Abstract—This paper introduces a Bayesian framework to detect multiple signals embedded in noisy obs...
This paper considers the problem of compact source detection on a Gaussian background in 1D. Two asp...
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) t...
For several reasons, Bayesian parameter estimation is superior to other methods for extracting featu...
The design approach presented in this paper applies Bayesian inference to the design of finite impul...
The design approach presented in this paper applies Bayesian inference to the design of finite impul...
In this dissertation we explore theoretical and computational methods to investigate Bayesian ideal ...
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed ...
In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clut...
International audienceThis paper introduces a Bayesian framework to detect multiple signals embedded...
International audienceWe consider the adaptive detection of a signal of interest embedded in colored...
International audienceWe derive the expression of an optimum non-Gaussian radar detector from the no...
In this paper, a theoretical expression of the optimum non-Gaussian radar detector is derived from t...
We derive the expression of an optimum non-Gaussian radar detector from the non-Gaussian spherically...
This letter focuses on the design of selective receivers for homogeneous scenarios where a very smal...
Abstract—This paper introduces a Bayesian framework to detect multiple signals embedded in noisy obs...